A game theoretic approach to explain the output of any machine learning model.

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.

A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.

Fast SHAP value computation for interpreting tree-based models

利用lightgbm做(learning to rank)排序学习，包括数据处理、模型训练、模型决策可视化、模型可解释性以及预测等。Use LightGBM to learn ranking, including data processing, model training, model decision visualization, model interpretability and prediction, etc.

Shapley Interactions for Machine Learning

A power-full Shapley feature selection method.

TimeSHAP explains Recurrent Neural Network predictions.

Automated Tool for Optimized Modelling

Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)

Validation (like Recursive Feature Elimination for SHAP) of (multiclass) classifiers & regressors and data used to develop them.

Explainable Machine Learning in Survival Analysis

A Julia package for interpretable machine learning with stochastic Shapley values

streamlit-shap provides a wrapper to display SHAP plots in Streamlit.

SurvSHAP(t): Time-dependent explanations of machine learning survival models

Compute SHAP values for your tree-based models using the TreeSHAP algorithm

Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)

SHAP Plots in R